Course Offerings — Winter Term 2026/2027

Each MSc main module (ML 1, ML 2, DL 1, DL 2) comes in a standard variant (9 CP) and an -X variant (+3 CP) that adds one elective. CA (BSc) includes one elective. Electives only count as part of an -X module or CA, not standalone. Note: PyML is now called MLE.

Which electives fit which module? (WiSe 2026 offering; electives count only as the +3 CP part of an -X module or of CA)

TODO

Recommended paths

• BSc: start with CA
• MSc:
  • No CS background: take introductory CS elsewhere
  • Little math background: take introductory math first elsewhere, plan MathML as your elective
  • Little engineering background: take MLE
  • Solid math/engineering background: take ML/DL (advanced ML) or MLE (advanced engineering)
  • Projects: complete ML or DL and MLE first (or equivalent)

Deep Learning 1 is a course covering the foundations of deep learning. This includes the basics of neural networks and introductions to established architectures such as convolutional and recurrent neural networks. ML 1 and 2 are both recommended prerequisites for this course. Lectures will cover the following topics:

• Backpropagation
• Optimization
• Regularization
• Loss Functions
• Convolutional Networks
• Recurrent Neural Networks
• Autoencoders
• Structured Output
• Explainable AI

This module was renamed from “Python for Machine Learning” to “Machine Learning Engineering” in the winter term 2026/2027. Students who passed PyML cannot take MLE, because both are the same module.

The module “Intelligent Biomedical Sensing 1: Signal Acquisition and ML” takes students from the physiological origin of a biosignal to a working machine-learning pipeline. It combines the lecture “Machine Learning for Biomedical Signal Analysis”, which covers the fundamentals of electrophysiological and optical biosignals, time-series preprocessing, decomposition methods, feature extraction and multimodal fusion, with the “Biomedical Sensing” project. In the project, small teams build their own ESP32-based acquisition hardware (ECG, PPG, motion), train classical ML models on a real biomedical dataset, and validate them on data they record themselves. Project topics include cuff-less blood pressure estimation, respiratory rate estimation, stress classification and heart rate estimation during motion. Prior or simultaneous hearing of ML1 lecture is a prerequisite. Lecture and project can only be taken together.

Seminar on Machine learning for Neuroscience. For successful participation in the seminar, basic background in neuroscience and motivation to learn about neuroscientific topics are highly recommended. This semester the focus will be on Continual Learning! Please NOTE that this seminar is a standalone module and NOT an elective.

Introduction to the Julia programming language and its Machine Learning ecosystem. Learn how to write reproducible, unit-tested Julia code for ML research in Julia. No prior knowledge of Julia is required.

This is a joint, research-oriented seminar by the Machine Learning Group and the Data Management Group. Throughout the seminar, students will have the opportunity to learn about recent advances at the intersection of Machine Learning and Data Management Systems. Interested students are required to participate in the kick-off meeting, after which they will select, read, understand, and present one of the eligible papers. Moreover, the students will be required to submit a 3-slide slide deck summarizing their selected paper as a midterm examination. The final presentation, lasting 15 minutes (10 minutes presentation + 5 minutes of questions), will be held in English at the end of the semester (the exact date will be announced). Only the final presentation will be considered for the student’s final grade. More details will be discussed during the kick-off meeting. Note that as of the summer term 2024, this seminar is offered as an elective or standalone module.

This is a research-oriented seminar about applications of machine learning to quantum chemistry. Students will read, understand, evaluate and present selected research papers on machine learning methods in quantum chemistry. At the end of the semester, each student will present their topic in a 20 min talk (+ 10 min questions) in English. It is possible to attend this course without prior knowledge in chemistry or physics since many papers only require a basic comprehension of the respective research topic. There is no formal registration for the kick-off meeting. In the general case, it is not possible to take the seminar as a standalone course.

In this seminar, foundational and recent research in generative modelling is studied. Students will present and discuss selected papers from the field.